Age, growth, and intrinsic sensitivity of Endangered Spinetail Devil Ray (Mobula mobular) and Bentfin Devil Ray (M. thurstoni) in the Indian Ocean
Bibliographic record
Abstract
Abstract Devil rays ( Mobula spp.) are caught in fisheries across the Indian Ocean, with reports of significant recent declines in catch and sightings. Globally, the few populations studied have extremely low population growth rates due to low fecundity and long reproductive cycles, making them highly vulnerable to overfishing. To allow for assessment of the current sustainability of devil ray catch in the Indian Ocean, we provide estimates of age using the caudal vertebrae; somatic growth using a Bayesian, multi-model approach; maximum intrinsic rate of population increase ( r max ); and fishing mortality for Endangered Spinetail Devil Ray ( Mobula mobular ) and Bentfin Devil Ray ( M. thurstoni ) sampled from small-scale fisheries catch in Indonesia, Kenya, and Pakistan. The oldest individuals of Spinetail Devil Ray ( n = 79) and Bentfin Devil Ray ( n = 59) were 17.5 and six years, respectively. Both species had relatively low growth coefficients ( k = 0.05 and g = 0.19 year −1 , respectively), with the von Bertalanffy and logistic models providing the best fitting growth models, and low r max (0.109 and 0.107 year −1 , respectively) indicating that they are highly sensitive to overexploitation. Fishing mortality F estimates (0.16 and 0.18 year −1 , respectively) were higher than r max and exploitation ratio E (0.77 and 0.80, respectively) were higher than an optimum value of 0.5 for biological sustainability for both species, suggesting that the fisheries catches of the species are unsustainable. We demonstrate an approach to assess data-poor species and apply this to two Indian Ocean devil ray species. The results highlight the urgent need for better management actions to reduce the catch of all devil rays to prevent species extinction and aid in population recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".